miuul_summercamp_1_8july.ipynb

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      "name": "miuul.summercamp.1-8july.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
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          "data": {
            "text/plain": [
              "False"
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          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "5 ==4"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "5==5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1J01UM_4xD2",
        "outputId": "18398c9b-3224-4365-dfbc-6953545405cb"
      },
      "execution_count": 2,
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          "data": {
            "text/plain": [
              "True"
            ]
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          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sözlük \n",
        "\n",
        "x = {\"name\":\"Peter\",\n",
        "     \"age\": \"26\"}\n",
        "print(x)\n",
        "type(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIIgNSi-41F2",
        "outputId": "eb0b7c60-4977-4dba-d983-0cc3ec773e0f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'name': 'Peter', 'age': '26'}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#tuple \n",
        "\n",
        "x=(\"python\",\"ml\",\"ds\",0)\n",
        "type(x)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "g4rWW7oJ8Xmf",
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            "text/plain": [
              "tuple"
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "code",
      "source": [
        "dir(int)"
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        "outputId": "894c9fd9-cb6c-42d9-d3c7-855d3264ee23"
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          "data": {
            "text/plain": [
              "['__abs__',\n",
              " '__add__',\n",
              " '__and__',\n",
              " '__bool__',\n",
              " '__ceil__',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dir__',\n",
              " '__divmod__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__float__',\n",
              " '__floor__',\n",
              " '__floordiv__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getnewargs__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__index__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__int__',\n",
              " '__invert__',\n",
              " '__le__',\n",
              " '__lshift__',\n",
              " '__lt__',\n",
              " '__mod__',\n",
              " '__mul__',\n",
              " '__ne__',\n",
              " '__neg__',\n",
              " '__new__',\n",
              " '__or__',\n",
              " '__pos__',\n",
              " '__pow__',\n",
              " '__radd__',\n",
              " '__rand__',\n",
              " '__rdivmod__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__rfloordiv__',\n",
              " '__rlshift__',\n",
              " '__rmod__',\n",
              " '__rmul__',\n",
              " '__ror__',\n",
              " '__round__',\n",
              " '__rpow__',\n",
              " '__rrshift__',\n",
              " '__rshift__',\n",
              " '__rsub__',\n",
              " '__rtruediv__',\n",
              " '__rxor__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__sub__',\n",
              " '__subclasshook__',\n",
              " '__truediv__',\n",
              " '__trunc__',\n",
              " '__xor__',\n",
              " 'bit_length',\n",
              " 'conjugate',\n",
              " 'denominator',\n",
              " 'from_bytes',\n",
              " 'imag',\n",
              " 'numerator',\n",
              " 'real',\n",
              " 'to_bytes']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dir(str)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1p1rsdT_tdY",
        "outputId": "4fa28cac-065b-40f9-ae20-bbddda9f9e01"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__add__',\n",
              " '__class__',\n",
              " '__contains__',\n",
              " '__delattr__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getitem__',\n",
              " '__getnewargs__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__iter__',\n",
              " '__le__',\n",
              " '__len__',\n",
              " '__lt__',\n",
              " '__mod__',\n",
              " '__mul__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__rmod__',\n",
              " '__rmul__',\n",
              " '__setattr__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " 'capitalize',\n",
              " 'casefold',\n",
              " 'center',\n",
              " 'count',\n",
              " 'encode',\n",
              " 'endswith',\n",
              " 'expandtabs',\n",
              " 'find',\n",
              " 'format',\n",
              " 'format_map',\n",
              " 'index',\n",
              " 'isalnum',\n",
              " 'isalpha',\n",
              " 'isascii',\n",
              " 'isdecimal',\n",
              " 'isdigit',\n",
              " 'isidentifier',\n",
              " 'islower',\n",
              " 'isnumeric',\n",
              " 'isprintable',\n",
              " 'isspace',\n",
              " 'istitle',\n",
              " 'isupper',\n",
              " 'join',\n",
              " 'ljust',\n",
              " 'lower',\n",
              " 'lstrip',\n",
              " 'maketrans',\n",
              " 'partition',\n",
              " 'replace',\n",
              " 'rfind',\n",
              " 'rindex',\n",
              " 'rjust',\n",
              " 'rpartition',\n",
              " 'rsplit',\n",
              " 'rstrip',\n",
              " 'split',\n",
              " 'splitlines',\n",
              " 'startswith',\n",
              " 'strip',\n",
              " 'swapcase',\n",
              " 'title',\n",
              " 'translate',\n",
              " 'upper',\n",
              " 'zfill']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# len , str lere uygulanabilir uzunluk\n",
        "\n",
        "name = \"john\"\n",
        "type(name)\n",
        "type(len)\n",
        "\n",
        "#builtin_function_or_method   gömülü fonksiyonlar \n",
        "\n",
        "#python geliştiricilerinin önceden tanımlayıp dile gömülmüş faydalı bir takım araçlardır.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "7DrTgJ0j_xj9",
        "outputId": "66efc58e-82fa-4cc9-d6af-e98b00722bc3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "builtin_function_or_method"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(\"melisa\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W80erqVEADgC",
        "outputId": "aecbdc35-00c8-43a8-e9fd-89f54d11948b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bir fonksiyon class yapısı içinde tanımlandıysa buna method denir. Eğer class yapısı içinde değilse fonksiyondur."
      ],
      "metadata": {
        "id": "8qaIQ5_WApao"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"miuul\".upper()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ZLNTwmHxAlUY",
        "outputId": "4d2a46e3-ce91-463d-99f9-9696ced907b0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MIUUL'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"MELİSA\".lower()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Nuj193A6BQ3A",
        "outputId": "474b3ffa-789f-452f-b16f-b851e4ccea59"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'meli̇sa'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LİSTELER \n",
        "\n",
        "Değiştirilebilir\n",
        "sıralıdır. Index işlemleri yapılabilir\n",
        "kapsayıcıdır"
      ],
      "metadata": {
        "id": "Mjqr6LlJduL2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "notes = [1, 2, 3, 4]\n",
        "type(notes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBkoXZgdBVmR",
        "outputId": "d10d1b77-d42b-4ee1-b5bb-7167882c1f87"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = [1,2,\"a\",True,[1,2,3]]\n",
        "a[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPfqsi79eKPY",
        "outputId": "92674148-d1d3-4657-8632-eb4f29149253"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a[4][2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMRp8rCGgK8j",
        "outputId": "65e2c07b-2c96-4c95-a77e-a8803bce90c0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# liste metodları\n",
        "\n",
        "dir(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74ER1RGXge7C",
        "outputId": "45ee9b2f-bb5e-40bb-cda0-9f6a8c4c31b6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__add__',\n",
              " '__class__',\n",
              " '__contains__',\n",
              " '__delattr__',\n",
              " '__delitem__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getitem__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__iadd__',\n",
              " '__imul__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__iter__',\n",
              " '__le__',\n",
              " '__len__',\n",
              " '__lt__',\n",
              " '__mul__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__reversed__',\n",
              " '__rmul__',\n",
              " '__setattr__',\n",
              " '__setitem__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " 'append',\n",
              " 'clear',\n",
              " 'copy',\n",
              " 'count',\n",
              " 'extend',\n",
              " 'index',\n",
              " 'insert',\n",
              " 'pop',\n",
              " 'remove',\n",
              " 'reverse',\n",
              " 'sort']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJ0ZOfJ-gpne",
        "outputId": "9a3b767f-47b2-4b46-8c4d-67b73d5ac121"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "notes.append(100) #append\n",
        "notes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afWWYBKKgxsw",
        "outputId": "0f05adf2-1c9a-41a1-9f55-f0fcf4f96d6a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 100]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pop --> indexe göre siler\n",
        "\n",
        "notes.pop(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVvyUK4kg6a8",
        "outputId": "ce515b53-32c7-4235-f704-d51072136492"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "notes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ElSXjKchGSC",
        "outputId": "a832b3c4-3323-4891-ce8a-29214a9f73cc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 4, 100]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# insert --> hangi index'e ne eklemek istiyorsan onu yapar.\n",
        "\n",
        "notes.insert(2, 50)\n",
        "notes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20uUVdgchIRB",
        "outputId": "4101b439-e627-4668-c58c-bb3e1820236c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 50, 4, 100]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dictionary\n",
        "\n",
        "# değiştirilebilir\n",
        "# sırasız (python 3.7 sürümünden sonra sıralı)\n",
        "#kapsayıcı"
      ],
      "metadata": {
        "id": "VqTquQI-hf9K"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# key - value\n",
        "\n",
        "dictionary = {\"REG\": \"Regression\",\n",
        "              \"LOG\" : \"Logistic Regression\",\n",
        "              \"CART\" : \"Classification and Reg\"}"
      ],
      "metadata": {
        "id": "pFtYJqcNhte4"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# key sorgulama \n",
        "\n",
        "\"YSA\" in dictionary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6MsI13siB8R",
        "outputId": "de2ef418-3f15-49e9-a2a3-e550b2a90305"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary.get(\"REG\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MY5JZsx7iwwD",
        "outputId": "dbd82bd4-70db-4d02-a3b8-aa9a280d7535"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Regression'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# value değiştirmek\n",
        "\n",
        "dictionary[\"REG\"] = [\"YSA\", 10 ]\n",
        "dictionary\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CwJQwE5i2VB",
        "outputId": "299dd031-1517-4521-9014-68e14c1094cd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CART': 'Classification and Reg',\n",
              " 'LOG': 'Logistic Regression',\n",
              " 'REG': ['YSA', 10]}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tüm key lere erişmek \n",
        "dictionary.keys()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUyiDclOjMUJ",
        "outputId": "7aa35460-508f-4501-ff7e-b6717e90d9a5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['REG', 'LOG', 'CART'])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary.values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xvad3I1WjfPq",
        "outputId": "0e949c50-c753-450d-fe74-0695c54a65ff"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([['YSA', 10], 'Logistic Regression', 'Classification and Reg'])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dir(dictionary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjq3S1Eijj0E",
        "outputId": "fdae87d0-b463-4827-a6ff-de2af88ab57f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['__class__',\n",
              " '__contains__',\n",
              " '__delattr__',\n",
              " '__delitem__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattribute__',\n",
              " '__getitem__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__iter__',\n",
              " '__le__',\n",
              " '__len__',\n",
              " '__lt__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__setitem__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " 'clear',\n",
              " 'copy',\n",
              " 'fromkeys',\n",
              " 'get',\n",
              " 'items',\n",
              " 'keys',\n",
              " 'pop',\n",
              " 'popitem',\n",
              " 'setdefault',\n",
              " 'update',\n",
              " 'values']"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary.pop(\"REG\")\n",
        "dictionary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4PkHtv56k1gr",
        "outputId": "72c2714a-cc38-4c59-e58f-1a76099223e5"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CART': 'Classification and Reg', 'LOG': 'Logistic Regression'}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary.update({\"Ahmet\": [\"Turkey\", 24]})\n",
        "dictionary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hsdid5XilICx",
        "outputId": "41c00ac7-935e-423a-b4a9-b1dc16ad6e17"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Ahmet': ['Turkey', 24],\n",
              " 'CART': 'Classification and Reg',\n",
              " 'LOG': 'Logistic Regression'}"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TUPLE (DEMET)\n",
        "\n",
        "#DEĞİŞTİRİLEMEZ\n",
        "#SIRALIDIR\n",
        "#KAPSAYICIDIR (birden fazla eleman var)\n"
      ],
      "metadata": {
        "id": "o2UbcuoQlfCW"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t = (\"john\", \"mark\", 1, 2)\n",
        "type(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXSWM083m10G",
        "outputId": "dc6791aa-ff00-4367-9d51-e48eeade8d41"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tuple"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t[0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKmBMyzonJVb",
        "outputId": "5a45ef9a-f24d-412a-b2cd-3319aea39bce"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('john', 'mark')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t[0] = 99 #değiştirilemez olduğu örneği"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "tt0RygacnKwz",
        "outputId": "5854a246-ac79-463f-f396-98ba69c70fa9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-9c37e9665bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m \u001b[0;31m#değiştirilemez olduğu örneği\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set --> kümeler \n",
        "#difference --> iki küme farkı \n",
        "\n",
        "set1 = set([1, 3, 5])\n",
        "set2 = set([1, 2, 3])\n",
        "\n",
        "#set1 de olup set2 de olmayan\n",
        "\n",
        "set1.difference(set2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVGee2gmnPGZ",
        "outputId": "bcf4a680-e1ba-46be-d696-2183caa2747f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{5}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#intersect \n",
        "set1.intersection(set2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QPOqeRKDsDxC",
        "outputId": "02f32a0a-38bc-4b88-87da-df560d772dc5"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 3}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(set1 & set2) #matematiksel işlemlerde yapabilirsin\n",
        "print(set1 - set2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kh4PsOtKspcj",
        "outputId": "620a9efb-65ec-4d43-986c-e698b4c107be"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1, 3}\n",
            "{5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# isdisjoint() : İki kümenin kesişimi boş mu ?\n",
        "set1.isdisjoint(set2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGSv75Desx-V",
        "outputId": "38178b75-aee6-4bbc-9002-01196abafb15"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set1.issubset(set2) # set1 set2 nin alt kümesi mi ?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhroLUaNtGWf",
        "outputId": "7255a2f5-aa0f-42d3-f5ec-41277606f3a8"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set1.issuperset(set2) # set1 set2 yi kapsıyor mu ?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HX0Vw-D0tSdE",
        "outputId": "1f6a636e-4c49-4f63-df74-a9fdd5d895d7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FONKSİYONLAR"
      ],
      "metadata": {
        "id": "vBI40i7MgmQA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "?print #parametrede argümanda doğrudur \n",
        "#parametre : fonksiyon tanımlanması esnasında ifade edilmesi gereken değişkenlerdir\n",
        "#argüman: fonk çağırıldığında parametre değerlerine karşılık girilen değerlerdir"
      ],
      "metadata": {
        "id": "Ufxq8LyQtfh_"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Docstring:\n",
        "print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n",
        "\n",
        "Prints the values to a stream, or to sys.stdout by default.\n",
        "Optional keyword arguments:\n",
        "\n",
        "file:  a file-like object (stream); defaults to the current sys.stdout.\n",
        "\n",
        "sep:   string inserted between values, default a space.\n",
        "\n",
        "end:   string appended after the last value, default a newline.\n",
        "\n",
        "flush: whether to forcibly flush the stream.\n",
        "\n",
        "Type:      builtin_function_or_method"
      ],
      "metadata": {
        "id": "5CsVTG0p4yCG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"a\", \"b\", sep = \"__\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ElWzol1gt-c",
        "outputId": "0e4204b6-d448-41f6-daf1-8fe093ece235"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a__b\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "help(print)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lb84nJRahpkB",
        "outputId": "acd02bbb-6213-4edd-ac72-e429ddbe62f4"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on built-in function print in module builtins:\n",
            "\n",
            "print(...)\n",
            "    print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n",
            "    \n",
            "    Prints the values to a stream, or to sys.stdout by default.\n",
            "    Optional keyword arguments:\n",
            "    file:  a file-like object (stream); defaults to the current sys.stdout.\n",
            "    sep:   string inserted between values, default a space.\n",
            "    end:   string appended after the last value, default a newline.\n",
            "    flush: whether to forcibly flush the stream.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a =int(input(\"Bir sayı giriniz : \")) \n",
        "print(a*2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsNd0ap1h5fH",
        "outputId": "fa7f807f-4139-4268-c6bc-c0a64cd9a24d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bir sayı giriniz : 5\n",
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate(x):\n",
        "  print(x*2)\n",
        "calculate(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72ym-cxIiOsH",
        "outputId": "f7ef9a4f-6241-4671-a0c2-efd8bc2c2576"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = int(input(\"bir tam sayı giriniz :\"))\n",
        "def cal(a):\n",
        "  print(a*2)\n",
        "cal(a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wndPFjr_jn5Z",
        "outputId": "ee6b447b-4b7d-4aa8-f4e3-3e69be720393"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bir tam sayı giriniz :25\n",
            "50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "iki argümanlı /parametreli bir fonksiyon tanımlayalım "
      ],
      "metadata": {
        "id": "WEjhuN5Hjh6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def summer(arg1, arg2):\n",
        "  print(arg1 + arg2)\n",
        "summer(7, 8)\n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bw9VCZr4jRyp",
        "outputId": "7fd513a3-3aaf-4fa7-afa2-ef4a1c0d43ec"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Docstring --> herkesin anlayacağı dilden bilgi notu ekleme methodudur."
      ],
      "metadata": {
        "id": "khJu3svTl0lA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def summer(arg1, arg2):\n",
        "  print(arg1 + arg2)\n",
        "\n",
        "\n",
        "# 3 tırnak ile \"\"\" \"\"\" pycharm da bunu yapıp entr a bastığında parametreler gelir"
      ],
      "metadata": {
        "id": "RCXhzye4lR3K"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fonksiyonların Statement/Body Bölümü\n",
        "\n",
        "# def function_name(parameters/arguments):\n",
        "#          statements(function body)"
      ],
      "metadata": {
        "id": "2Sc089CwpyFB"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def say_hi():\n",
        "  print(\"Merhaba\")\n",
        "  print(\"Hi\")\n",
        "  print(\"Hello\")\n",
        "say_hi()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ox16vf5QtNqM",
        "outputId": "0dbbb790-fbcd-425a-d8aa-5b8ba135bb17"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merhaba\n",
            "Hi\n",
            "Hello\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def say_hi(string):\n",
        "  print(string)\n",
        "  print(\"Hi\")\n",
        "  print(\"Hello\")\n",
        "say_hi(\"miuul\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgDOmEVUtaTn",
        "outputId": "cd849d2f-9bfd-4174-d36b-8b888e0d93c4"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "miuul\n",
            "Hi\n",
            "Hello\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def multiplication(a, b):\n",
        "  c = a * b\n",
        "  print(c)\n",
        "multiplication(9, 50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRP1ligFtq47",
        "outputId": "3915ce47-ba71-465a-8eea-82ede225d960"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "450\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Girilen 2 sayının çarpımını boş bir listeye sırasıyla ekleyecek fonksiyon yazın\n",
        "list_store = []\n",
        "\n",
        "def add_element(a, b):\n",
        "  c = a * b\n",
        "  list_store.append(c)\n",
        "  print(list_store)\n",
        "\n",
        "add_element(8, 9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0O72_4Jbt9tp",
        "outputId": "3f737b36-31ba-4201-b169-52e89b2eef47"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[72]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "add_element(2, 8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xq2Bl-6_uxaK",
        "outputId": "0ec5d40b-f6ed-40b4-85f1-9663cecf016e"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[72, 16]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#return --> Fonksiyon çıktılarını girdi olarak kullanır\n",
        "\n",
        "def calculate(varm, moisture, charge):\n",
        "  print((varm + moisture)/charge)\n",
        "\n",
        "calculate(90, 12, 78) * 10 #nonetype ile str çarpılmaz ! aman dikkat !"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "DktIdywau4_B",
        "outputId": "37cc4c25-1a64-45f0-86ec-09b1d72799b2"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3076923076923077\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-44a13d56db6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvarm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmoisture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mcharge\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mcalculate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m78\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;31m#nonetype ile str çarpılmaz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'NoneType' and 'int'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate(varm, moisture, charge):\n",
        "  return (varm + moisture)/charge\n",
        "\n",
        "calculate(90, 12, 78) * 20  # bunun içim return kullanıyor aman dikkat returnden sonra yazılan kodlar çalışmaz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1rMr96BxQWT",
        "outputId": "73b48803-e01b-4080-94e8-58a71ace3dbd"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.153846153846153"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate(varm, moisture, charge):\n",
        "\n",
        "  varm = varm * 2\n",
        "  moisture = moisture * 2\n",
        "  charge = charge * 2\n",
        "  output = (varm + moisture)/charge\n",
        "  \n",
        "  return varm, moisture, charge, output\n",
        " \n",
        "calculate(90, 12, 78) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BibhjnZwxdTp",
        "outputId": "c36f2f7b-cd13-4584-a996-69af64d5fecf"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180, 24, 156, 1.3076923076923077)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fonksiyon içinde fonksiyon çağırmak "
      ],
      "metadata": {
        "id": "unFKxxI2yVpH"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate(varm, moisture, charge):\n",
        "  return int((varm + moisture)/charge)\n",
        "\n",
        "calculate(90, 12, 78) * 20\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QW6qKIA-27dp",
        "outputId": "db7c1589-1b22-432b-b9a0-7e666a3dfac6"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def standardization(a, p):\n",
        "  return a*10 / 100 * p * p\n",
        "\n",
        "standardization(45, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hh6WjozY89ie",
        "outputId": "fa44c868-5cf9-41f6-8be4-8f25a9fae19a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.5"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def all_calculation(varm, moisture, charge, p):\n",
        "  a = calculate(varm, moisture, charge)\n",
        "  b = standardization(a, p)\n",
        "  print(b*10)\n",
        "\n",
        "all_calculation(1, 3, 5, 128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNd8EUGI27qS",
        "outputId": "d5a5422f-9f31-48ce-adad-0ebaebace128"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def all_calculation(varm, moisture, charge, a, p):\n",
        "  print(calculate(varm, moisture, charge))\n",
        "  b = standardization(a, p)\n",
        "  print(b*10)\n",
        "\n",
        "all_calculation(1, 3, 5, 19, 128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4wZHz-dV27sm",
        "outputId": "67bd88e4-71c7-41e6-a419-32355f63cee5"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "311296.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list_store = [1, 2]           #global etki alanı\n",
        "def add_element(a, b):\n",
        "  c = a * b                #fonksiyon içi local etki alanı\n",
        "  list_store.append(c)    \n",
        "  print(list_store)\n",
        "\n",
        "add_element(2, 9)  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xiz5-j39TUn",
        "outputId": "1e4d302b-8b59-4023-d7dd-cd87f4948bca"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2, 18]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KOŞULLAR (CONDITIONS)"
      ],
      "metadata": {
        "id": "BW2_Yqw--igX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if 1 == 1:        #bu işlemin sonucu doğru ise içindeki değeri döndürür\n",
        "  print(\"something\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmVTalg6_EFn",
        "outputId": "62ba1206-2f00-4940-f9bb-9f17622d54ec"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "something\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if 1 == 2:\n",
        "  print(\"something\")\n",
        "\n",
        "else:\n",
        "  print(\"nothing\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "liCJHVUC-KLD",
        "outputId": "d506ab21-4693-437e-a827-7142534d2a25"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nothing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def number_check(number):\n",
        "  if number == 10:\n",
        "    print(\"number is 10\")\n",
        "number_check(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LaWmZPS-6YH",
        "outputId": "fb281f5d-e902-446b-fb2b-3b44b95c711c"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number is 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def number_check(number):\n",
        "  if number == 10:\n",
        "    print(\"number is 10\")\n",
        "  else:\n",
        "    print(\"number is not 10\")\n",
        "number_check(120)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8NVbCqx_n-J",
        "outputId": "c4d463a6-f8f0-400f-eaaa-9a47d8604939"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number is not 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def number_check(number):\n",
        "  if number > 10:\n",
        "    print(\"greater than 10\")\n",
        "  elif number < 10:\n",
        "    print(\"less than 10\")\n",
        "  else:\n",
        "    print(\"equal to 10\")\n",
        "\n",
        "number_check(10)    \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjZc4acV_s56",
        "outputId": "9d061e44-cb13-46dd-85ac-40b42cd1f059"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "equal to 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def number_check(number):\n",
        "  if number > 10:\n",
        "    print(\"greater than 10\")\n",
        "  elif number < 10:\n",
        "    print(\"less than 10\")\n",
        "  else:\n",
        "    print(\"equal to 10\")\n",
        "\n",
        "number_check(15) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vh9oLsUAk7B",
        "outputId": "aebaaead-04e3-4210-e78b-7e266bf482ee"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "greater than 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DÖNGÜLER"
      ],
      "metadata": {
        "id": "lq2rWcmTA0f1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# FOR \n",
        "students = [\"John\", \"Mark\", \"Vanessa\", \"Mariam\"]\n",
        "\n",
        "for student in students:\n",
        "  print(student)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlaeN3-rAzOA",
        "outputId": "8f6836fb-aa0b-43d6-d7bc-f06c3f40c70f"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "John\n",
            "Mark\n",
            "Vanessa\n",
            "Mariam\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for student in students:\n",
        "  print(student.upper())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpBu0AshFbVl",
        "outputId": "69a24eee-00ed-4bb9-e8b8-efbff72d57dc"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "JOHN\n",
            "MARK\n",
            "VANESSA\n",
            "MARIAM\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def new_salary(salary, rate):\n",
        "  return int(salary*rate/100 + salary)\n",
        "new_salary(9300, 44.85)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9AdnvPYFhXz",
        "outputId": "d4546c36-a078-4139-c02c-7a909f4fc2e4"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13471"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_salary(3300, 42.35) - 2500"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRVWwMQmJPAz",
        "outputId": "b920ac5c-ca78-4680-8294-099b336be87d"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2197"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "salaries = [1000, 2000, 3000, 4000]\n",
        "for salary in salaries:\n",
        "  print(new_salary(salary, 10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wEfxBmeYJVZI",
        "outputId": "8d54486a-3218-4cd1-d83b-f2f058a1b792"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1100\n",
            "2200\n",
            "3300\n",
            "4400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for salary in salaries:\n",
        "  if salary >= 3000:\n",
        "    print(new_salary(salary, 10))\n",
        "  else:\n",
        "    print(new_salary(salary, 20))  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTKxQsSQcR6H",
        "outputId": "0a9a30a7-5b4b-4716-ab4c-dade6fa84545"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1200\n",
            "2400\n",
            "3300\n",
            "4400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(\"miuul\")):\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYe65uMTgpqX",
        "outputId": "b4207e66-e4a4-4ef4-8fd5-0afc10ea6744"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MÜLAKAT SORUSU\n",
        "\n",
        "Aşağıdaki şekilde string değiştiren fonksiyon yazmak istiyoruz.\n",
        "\n",
        "before : \"hi my name is john and ı am learning python\"\n",
        "\n",
        "after : \"Hi mY NaMe iS JoHn aNd ı aM LeArNiNg pYtHoN\""
      ],
      "metadata": {
        "id": "IpwRGHEWdaLr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def alternating(string):\n",
        "  new_string = \"\"   #girilen string'in indexlerinde gez\n",
        "  for string_index in range(len(string)):    #index çift ise büyük harfe çevir\n",
        "    if string_index % 2 == 0:\n",
        "      new_string += string[string_index].upper()\n",
        "    else:                                   #index tek ise küçük harfe çevir\n",
        "      new_string += string[string_index].lower()\n",
        "  print(new_string)\n",
        "\n",
        "alternating(\"hi my name is john and i am learning python\")  \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDE-lqxBdCOw",
        "outputId": "fd4ba667-0123-4ee2-d453-7bf6225c521e"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hi mY NaMe iS JoHn aNd i aM LeArNiNg pYtHoN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BREAK & CONTINUE & WHILE"
      ],
      "metadata": {
        "id": "N2PE1eUkj9ik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "salaries = [1000, 2000, 3000, 4000, 5000]\n",
        "\n",
        "for salary in salaries:\n",
        "  if salary == 3000:\n",
        "    break\n",
        "  print(salary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPVLzVmhj9Mi",
        "outputId": "bcf9074a-4b84-4149-d979-3e8e51b52c05"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n",
            "2000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for salary in salaries:\n",
        "  if salary == 3000:\n",
        "    continue\n",
        "  print(salary)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyftHJd_eYG4",
        "outputId": "b66aa00e-f1a5-4954-b561-0c9b8be2d334"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n",
            "2000\n",
            "4000\n",
            "5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number = 1 \n",
        "\n",
        "while number < 5:\n",
        "  print(number)\n",
        "  number +=1 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WP9JADc_lwGF",
        "outputId": "f958f9ad-ae39-4d22-f675-1daccb882a60"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ENUMERATE : OTOMATİK COUNTER/INDEXLER İLE FOR LOOP"
      ],
      "metadata": {
        "id": "xbt-MRkomdfd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "students = [\"John\", \"Mark\", \"Venessa\", \"Mariam\"]\n",
        "\n",
        "for student in students:\n",
        "  print(student)\n",
        "for index, student in enumerate(students):\n",
        "  print(index, student)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7Na4y7XmIJs",
        "outputId": "0c22f304-24a4-4733-ce87-3ffcf86042a1"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "John\n",
            "Mark\n",
            "Venessa\n",
            "Mariam\n",
            "0 John\n",
            "1 Mark\n",
            "2 Venessa\n",
            "3 Mariam\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for index, student in enumerate(students, 1):\n",
        "  print(index, student)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33un59len945",
        "outputId": "a5af670a-d341-4601-9eba-f3ca78f35c79"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 John\n",
            "2 Mark\n",
            "3 Venessa\n",
            "4 Mariam\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "çift indexteki öğrencileri bir listeye, tek indexteki öğrencileri bir başka listeye kaydet"
      ],
      "metadata": {
        "id": "4gx9QZCGoQC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "A = []\n",
        "B = []\n",
        "\n",
        "for index, student in enumerate(students):\n",
        "  if index % 2 == 0:\n",
        "    A.append(student)\n",
        "  else:\n",
        "    B.append(student)\n",
        "\n",
        "print(A)\n",
        "print(B)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkI1wWV2oC8v",
        "outputId": "48098063-694d-480a-ec39-a8d0e14865d8"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['John', 'Venessa']\n",
            "['Mark', 'Mariam']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "çift indexteki öğrencileri bir listeye, tek indexteki öğrencileri bir başka listeye kaydet \n",
        "\n",
        "fakat bu iki liste tek bir liste olarak return olsun"
      ],
      "metadata": {
        "id": "KFQZVlELqLge"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "students = [\"John\", \"Mark\", \"Venessa\", \"Mariam\"]\n",
        "\n",
        "def divide_students(students):\n",
        "  groups = [[], []]\n",
        "  for index, student in enumerate(students):\n",
        "    if index % 2 == 0:\n",
        "      groups[0].append(student)\n",
        "    else:\n",
        "      groups[1].append(student)\n",
        "  print(groups)\n",
        "  return groups\n",
        "\n",
        "st = divide_students(students)\n",
        "st[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvTeMpltowp6",
        "outputId": "06d3ece7-d7fb-46a3-c685-f18703be0ebe"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['John', 'Venessa'], ['Mark', 'Mariam']]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['John', 'Venessa']"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def alternating_with_enumerate(string):\n",
        "  new_string = \"\"\n",
        "  for i, letter in enumerate(string):\n",
        "    if i % 2 == 0:\n",
        "      new_string += letter.upper()\n",
        "    else:\n",
        "      new_string += letter.lower()\n",
        "  print(new_string)\n",
        "\n",
        "alternating_with_enumerate(\"hi my name is john and i am learning python\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1leCTB4rnrt",
        "outputId": "e1f9850d-1fec-4da3-eb6e-76f99c2af8f4"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hi mY NaMe iS JoHn aNd i aM LeArNiNg pYtHoN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ZIP (ayrı listeleri tek bir liste haline getirdiği için hayat kurtaran serisinden)"
      ],
      "metadata": {
        "id": "lmOupkhDtVIr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "students = [\"John\", \"Mark\", \"Venessa\", \"Mariam\"]\n",
        "\n",
        "departments = [\"math\", \"statistic\", \"physics\", \"astronomy\"]\n",
        "\n",
        "ages = [23, 30, 26, 22]\n",
        "\n",
        "list(zip(students, departments, ages))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "piIh4IwVs1fE",
        "outputId": "f10e5140-a712-4364-97ae-e1f51ff5f300"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('John', 'math', 23),\n",
              " ('Mark', 'statistic', 30),\n",
              " ('Venessa', 'physics', 26),\n",
              " ('Mariam', 'astronomy', 22)]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# lambda map filter reduce"
      ],
      "metadata": {
        "id": "vQwLs3GGRYcG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lambda bir fonksiyondur kullan at şeklindedir yani ihtiyacın olan bölümde kullan.\n",
        "\n",
        "new_sum = lambda a, b: a + b\n",
        "\n",
        "new_sum(8, 12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pWChZYHntqzz",
        "outputId": "f6715148-f5e8-45fb-8bd0-3f42cdf81701"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# map ben seni döngü yazmaktan kurtarıyorum der.\n",
        "\n",
        "salaries = [1000, 2000, 3000, 4000, 5000]\n",
        "\n",
        "def new_salary(x):\n",
        "  return x * 20 / 100 + x\n",
        "new_salary(5000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jyx6ZlUFCpuB",
        "outputId": "1e8457ab-4d83-43ae-f60d-fd9d1e8231c4"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6000.0"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(map(new_salary, salaries))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTwbXpI-S5MD",
        "outputId": "f341368a-8459-425c-8573-8219b30ac8e1"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1200.0, 2400.0, 3600.0, 4800.0, 6000.0]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(map(lambda x: x * 20 / 100 + x, salaries))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoxwVdXWTYs7",
        "outputId": "9dc4f4d7-c192-4f6d-cf03-7af4e9563366"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1200.0, 2400.0, 3600.0, 4800.0, 6000.0]"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# filter\n",
        "\n",
        "list_store = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "list(filter(lambda x: x % 2 == 0, list_store))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEYFLvqZTq7-",
        "outputId": "ce849dbc-7baa-45de-c970-7efa3fffaad4"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6, 8, 10]"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reduce\n",
        "from functools import reduce"
      ],
      "metadata": {
        "id": "JEQK3tkhUGW0"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list_store = [1, 2, 3, 4]\n",
        "\n",
        "reduce(lambda a, b: a + b, list_store)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oy-VJ60LUOvY",
        "outputId": "2c90d40c-4742-464a-c436-b60f37f884d8"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# COMPREHENSIONS\n",
        "\n",
        "Birden fazla satırda yapacağın işlemleri tek satırda yapmanı sağlar.\n",
        "\n",
        "##LIST COMPREHENSION"
      ],
      "metadata": {
        "id": "QHPtWSatU49f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "salaries = [1000, 2000, 3000, 4000, 5000]\n",
        "\n",
        "[new_salary(salary * 2 ) if salary < 3000 else new_salary(salary) for salary in salaries]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8H3uSo-Uo2D",
        "outputId": "47256792-7d74-432d-d159-bc87b8e54720"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2400.0, 4800.0, 3600.0, 4800.0, 6000.0]"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[salary * 2 for salary in salaries]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUj0jKIAWKjh",
        "outputId": "8741eccf-e00e-4be6-84ff-5536764bf118"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2000, 4000, 6000, 8000, 10000]"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "comprehension da if tek başına ise en sağda olur. eğer else ile birlikte ise o zaman for bloğu sağ tarafta olur. !"
      ],
      "metadata": {
        "id": "A-2JU-sGZbsp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "[salary * 2 for salary in salaries  if salary < 3000]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNnV3oG5Wbfr",
        "outputId": "b0c04972-f489-4dd7-a015-8a75226112bc"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2000, 4000]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[salary * 2 if salary < 3000 else salary*0 for salary in salaries ]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyACfXqBYsT5",
        "outputId": "f6294ffe-b79d-4c1b-c9f2-304c5f31a881"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2000, 4000, 0, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SORU\n",
        "\n",
        "Elinde iki liste var istenmeyen öğrencilerin olduğu listedekilerin isimlerini küçük harfle yaz. İlk listede ikinciden farklı ismi olanların isimlerini büyük harfle yaz !"
      ],
      "metadata": {
        "id": "zQbIdbyoaBGE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "students = [\"John\", \"Mark\",  \"Venessa\", \"Mariam\"]\n",
        "\n",
        "students_no = [\"John\", \"Vanessa\"]\n",
        "\n"
      ],
      "metadata": {
        "id": "KQUhkDcDZryt"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "students = [\"John\", \"Mark\",  \"Venessa\", \"Mariam\"]\n",
        "students_no = [\"John\", \"Venessa\"]\n",
        "\n",
        "[student.lower() if student in students_no else student.upper() for student in students]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rKTY6xxcEZ7",
        "outputId": "7c295e75-8cda-466a-9c56-3b37a6c03808"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['john', 'MARK', 'venessa', 'MARIAM']"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# dict_comprehension"
      ],
      "metadata": {
        "id": "MQGHba7flYUI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary = {\"a\" : 1,\n",
        "              \"b\" : 2,\n",
        "              \"c\" : 3,\n",
        "              \"d\" : 4 }\n",
        "print(dictionary.keys())\n",
        "print(dictionary.values())\n",
        "dictionary.items()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uKuh6ghcG79",
        "outputId": "c61a7ed1-e9c9-4e33-d51d-e41e260c0a47"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['a', 'b', 'c', 'd'])\n",
            "dict_values([1, 2, 3, 4])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('a', 1), ('b', 2), ('c', 3), ('d', 4)])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "{k : v**2 for (k, v) in dictionary.items()}    ## önemlii"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vv873yeklrT4",
        "outputId": "5c77ee1e-2e30-4c1f-bd63-042102eb7b88"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a': 1, 'b': 4, 'c': 9, 'd': 16}"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "{k.upper() : v**2 for (k, v) in dictionary.items()}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oIGlDL13mDlT",
        "outputId": "36503ae4-becd-45ba-f219-6d000a001810"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': 1, 'B': 4, 'C': 9, 'D': 16}"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MÜLAKAT SORUSU 1\n",
        "\n",
        "AMAÇ : çift sayıların karesi alınarak bir sözlüğe eklenmek istenmektedir.\n",
        "key ler orijinal değer value ise değiştirilmiş değer olacak."
      ],
      "metadata": {
        "id": "6o2tDt63vNGp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "numbers = range(10)\n",
        "new_dict = {}\n",
        "for n in numbers:\n",
        "  if n % 2 == 0:\n",
        "    new_dict[n] = n**2\n",
        "new_dict    \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixQMsxqPu_x1",
        "outputId": "e6214fe5-a1a0-4834-f764-94205047bf61"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 0, 2: 4, 4: 16, 6: 36, 8: 64}"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "{n: n**2 for n in numbers if n % 2 == 0}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GBmvlw4wTio",
        "outputId": "68c11f77-0ab5-44a5-8ac7-9c1fb6e181af"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 0, 2: 4, 4: 16, 6: 36, 8: 64}"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bir veri setindeki değişkenlerin isimlerini değiştirmek"
      ],
      "metadata": {
        "id": "cETbexfn16XR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "[\"total\", \"speeding\", \"alcohol\", \"not distrected\", \"no_previous\", \"ins_premium\", \"ins_losses\", \"abbrev\"]\n",
        "\n",
        "#bütün değerleri büyük yap."
      ],
      "metadata": {
        "id": "wtsLxOt41uDl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e1c7e5d-3cd7-44a5-a4bd-475c42f52dd0"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['total',\n",
              " 'speeding',\n",
              " 'alcohol',\n",
              " 'not distrected',\n",
              " 'no_previous',\n",
              " 'ins_premium',\n",
              " 'ins_losses',\n",
              " 'abbrev']"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "df = sns.load_dataset(\"car_crashes\")\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdlkADKP2k0u",
        "outputId": "10fcbc35-81d0-47dd-df73-395e3c63ebc3"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['total', 'speeding', 'alcohol', 'not_distracted', 'no_previous',\n",
              "       'ins_premium', 'ins_losses', 'abbrev'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gYmYKYhW319f",
        "outputId": "82460482-7ceb-42b0-8226-dfc3512c311a"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total  speeding  alcohol  not_distracted  no_previous  ins_premium  \\\n",
              "0   18.8     7.332    5.640          18.048       15.040       784.55   \n",
              "1   18.1     7.421    4.525          16.290       17.014      1053.48   \n",
              "2   18.6     6.510    5.208          15.624       17.856       899.47   \n",
              "3   22.4     4.032    5.824          21.056       21.280       827.34   \n",
              "4   12.0     4.200    3.360          10.920       10.680       878.41   \n",
              "\n",
              "   ins_losses abbrev  \n",
              "0      145.08     AL  \n",
              "1      133.93     AK  \n",
              "2      110.35     AZ  \n",
              "3      142.39     AR  \n",
              "4      165.63     CA  "
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  print(col.upper())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WE6aXV05396k",
        "outputId": "5ad27204-9a84-462c-f3f9-399fd05a38ad"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TOTAL\n",
            "SPEEDING\n",
            "ALCOHOL\n",
            "NOT_DISTRACTED\n",
            "NO_PREVIOUS\n",
            "INS_PREMIUM\n",
            "INS_LOSSES\n",
            "ABBREV\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "kalıcı bir listede büyük harfle sakla\n"
      ],
      "metadata": {
        "id": "jtKzHery4LoC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "A = []\n",
        "for col in df.columns:\n",
        "  A.append(col.upper())\n",
        "A"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hf4Ku-an4FdQ",
        "outputId": "534f2c7e-c7d6-4530-9059-edc55655ccbb"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['TOTAL',\n",
              " 'SPEEDING',\n",
              " 'ALCOHOL',\n",
              " 'NOT_DISTRACTED',\n",
              " 'NO_PREVIOUS',\n",
              " 'INS_PREMIUM',\n",
              " 'INS_LOSSES',\n",
              " 'ABBREV']"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = A\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VpEJyvr24Q6-",
        "outputId": "a8af956d-8b3e-401f-baae-51533a924a79"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TOTAL  SPEEDING  ALCOHOL  NOT_DISTRACTED  NO_PREVIOUS  INS_PREMIUM  \\\n",
              "0   18.8     7.332    5.640          18.048       15.040       784.55   \n",
              "1   18.1     7.421    4.525          16.290       17.014      1053.48   \n",
              "2   18.6     6.510    5.208          15.624       17.856       899.47   \n",
              "3   22.4     4.032    5.824          21.056       21.280       827.34   \n",
              "4   12.0     4.200    3.360          10.920       10.680       878.41   \n",
              "\n",
              "   INS_LOSSES ABBREV  \n",
              "0      145.08     AL  \n",
              "1      133.93     AK  \n",
              "2      110.35     AZ  \n",
              "3      142.39     AR  \n",
              "4      165.63     CA  "
            ],
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              "      <td>784.55</td>\n",
              "      <td>145.08</td>\n",
              "      <td>AL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.1</td>\n",
              "      <td>7.421</td>\n",
              "      <td>4.525</td>\n",
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              "      <td>133.93</td>\n",
              "      <td>AK</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.6</td>\n",
              "      <td>6.510</td>\n",
              "      <td>5.208</td>\n",
              "      <td>15.624</td>\n",
              "      <td>17.856</td>\n",
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              "      <td>110.35</td>\n",
              "      <td>AZ</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>22.4</td>\n",
              "      <td>4.032</td>\n",
              "      <td>5.824</td>\n",
              "      <td>21.056</td>\n",
              "      <td>21.280</td>\n",
              "      <td>827.34</td>\n",
              "      <td>142.39</td>\n",
              "      <td>AR</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.0</td>\n",
              "      <td>4.200</td>\n",
              "      <td>3.360</td>\n",
              "      <td>10.920</td>\n",
              "      <td>10.680</td>\n",
              "      <td>878.41</td>\n",
              "      <td>165.63</td>\n",
              "      <td>CA</td>\n",
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              "  </tbody>\n",
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              "      </button>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e56d4706-28c4-4581-89c3-38d46b8d3811 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e56d4706-28c4-4581-89c3-38d46b8d3811');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# list comprehension ile çözümü\n",
        "import seaborn as sns\n",
        "df = sns.load_dataset(\"car_crashes\")\n",
        "df.columns = [col.upper() for col in df.columns]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SdWFrZ_x4kvD",
        "outputId": "5a18d5ff-79da-4614-d3cb-465d5156424c"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TOTAL  SPEEDING  ALCOHOL  NOT_DISTRACTED  NO_PREVIOUS  INS_PREMIUM  \\\n",
              "0   18.8     7.332    5.640          18.048       15.040       784.55   \n",
              "1   18.1     7.421    4.525          16.290       17.014      1053.48   \n",
              "2   18.6     6.510    5.208          15.624       17.856       899.47   \n",
              "3   22.4     4.032    5.824          21.056       21.280       827.34   \n",
              "4   12.0     4.200    3.360          10.920       10.680       878.41   \n",
              "\n",
              "   INS_LOSSES ABBREV  \n",
              "0      145.08     AL  \n",
              "1      133.93     AK  \n",
              "2      110.35     AZ  \n",
              "3      142.39     AR  \n",
              "4      165.63     CA  "
            ],
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              "      <th></th>\n",
              "      <th>TOTAL</th>\n",
              "      <th>SPEEDING</th>\n",
              "      <th>ALCOHOL</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>18.8</td>\n",
              "      <td>7.332</td>\n",
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              "      <td>18.048</td>\n",
              "      <td>15.040</td>\n",
              "      <td>784.55</td>\n",
              "      <td>145.08</td>\n",
              "      <td>AL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.1</td>\n",
              "      <td>7.421</td>\n",
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              "      <td>17.014</td>\n",
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              "      <td>133.93</td>\n",
              "      <td>AK</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.6</td>\n",
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              "      <td>AZ</td>\n",
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              "      <th>3</th>\n",
              "      <td>22.4</td>\n",
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              "      <td>142.39</td>\n",
              "      <td>AR</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# UYGULAMA 2 \n",
        "\n",
        "bir önceki sorunun devamı olarak büyük harflendirilmiş kolonların içindeki ifadede eğer INS varsa başına FLAG ekle yoksa NO_FLAG ekle."
      ],
      "metadata": {
        "id": "lrGV2GAZ5NSe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.columns = [\"FLAG_\" + col if \"INS\" in col else \"NO_FLAG_\" + col for col in df.columns]\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kak56Pir48ga",
        "outputId": "d6781462-9472-4e33-82bc-1d5fcfaa85ed"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['NO_FLAG_TOTAL', 'NO_FLAG_SPEEDING', 'NO_FLAG_ALCOHOL',\n",
              "       'NO_FLAG_NOT_DISTRACTED', 'NO_FLAG_NO_PREVIOUS', 'FLAG_INS_PREMIUM',\n",
              "       'FLAG_INS_LOSSES', 'NO_FLAG_ABBREV'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#UYGULAMA 3 *** aşırı önemli\n",
        "\n",
        "AMAÇ : key i string, value aşağıdaki gibi bir liste olan sözlük oluşturmak.\n",
        "\n",
        "sadece sayısal değişkenler için bunu yap\n",
        "\n",
        "## sözlük comprehension yapısı"
      ],
      "metadata": {
        "id": "sijBQ5n67bdL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "df = sns.load_dataset(\"car_crashes\")\n",
        "df.columns\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kq-V6p7_52eA",
        "outputId": "dcbe1c5e-3552-4eb3-ac19-a54fe26e98b4"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['total', 'speeding', 'alcohol', 'not_distracted', 'no_previous',\n",
              "       'ins_premium', 'ins_losses', 'abbrev'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = [col for col in df.columns if df[col].dtype != \"O\"] #type object olanların haricindekileri alır\n",
        "num_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4aiTs5GWThIJ",
        "outputId": "48a7e0ba-958a-4860-f6a7-e518648e00d0"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['total',\n",
              " 'speeding',\n",
              " 'alcohol',\n",
              " 'not_distracted',\n",
              " 'no_previous',\n",
              " 'ins_premium',\n",
              " 'ins_losses']"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "soz = {}\n",
        "agg_list = [\"mean\", \"min\", \"max\", \"var\"]\n",
        "\n",
        "for col in num_cols:\n",
        "  soz[col] = agg_list\n",
        "soz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJkiMNslUAEO",
        "outputId": "42f77284-9964-48a2-b962-d1dfe6a947c1"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'alcohol': ['mean', 'min', 'max', 'var'],\n",
              " 'ins_losses': ['mean', 'min', 'max', 'var'],\n",
              " 'ins_premium': ['mean', 'min', 'max', 'var'],\n",
              " 'no_previous': ['mean', 'min', 'max', 'var'],\n",
              " 'not_distracted': ['mean', 'min', 'max', 'var'],\n",
              " 'speeding': ['mean', 'min', 'max', 'var'],\n",
              " 'total': ['mean', 'min', 'max', 'var']}"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# kısa yol\n",
        "\n",
        "new_dict = {col : agg_list for col in num_cols}\n",
        "new_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zx8QsZJoVmTr",
        "outputId": "911a039e-e8c6-45e0-c3d2-cd6ae9b9a324"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'alcohol': ['mean', 'min', 'max', 'var'],\n",
              " 'ins_losses': ['mean', 'min', 'max', 'var'],\n",
              " 'ins_premium': ['mean', 'min', 'max', 'var'],\n",
              " 'no_previous': ['mean', 'min', 'max', 'var'],\n",
              " 'not_distracted': ['mean', 'min', 'max', 'var'],\n",
              " 'speeding': ['mean', 'min', 'max', 'var'],\n",
              " 'total': ['mean', 'min', 'max', 'var']}"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[num_cols].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5QPTA4KzVyOT",
        "outputId": "f69118c3-38fa-41e3-ff7a-97b2f81a9822"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total  speeding  alcohol  not_distracted  no_previous  ins_premium  \\\n",
              "0   18.8     7.332    5.640          18.048       15.040       784.55   \n",
              "1   18.1     7.421    4.525          16.290       17.014      1053.48   \n",
              "2   18.6     6.510    5.208          15.624       17.856       899.47   \n",
              "3   22.4     4.032    5.824          21.056       21.280       827.34   \n",
              "4   12.0     4.200    3.360          10.920       10.680       878.41   \n",
              "\n",
              "   ins_losses  \n",
              "0      145.08  \n",
              "1      133.93  \n",
              "2      110.35  \n",
              "3      142.39  \n",
              "4      165.63  "
            ],
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              "        const buttonEl =\n",
              "          document.querySelector('#df-8450afd9-5a6e-4bba-8d99-d2d8ba01831b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8450afd9-5a6e-4bba-8d99-d2d8ba01831b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[num_cols].agg(new_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "KhvjNRQnW3DH",
        "outputId": "effbe2f8-83d9-4e70-eb60-3bdc30bf10b6"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          total  speeding    alcohol  not_distracted  no_previous  \\\n",
              "mean  15.790196  4.998196   4.886784       13.573176    14.004882   \n",
              "min    5.900000  1.792000   1.593000        1.760000     5.900000   \n",
              "max   23.900000  9.450000  10.038000       23.661000    21.280000   \n",
              "var   16.990902  4.071303   2.989901       20.330874    14.172755   \n",
              "\n",
              "       ins_premium  ins_losses  \n",
              "mean    886.957647  134.493137  \n",
              "min     641.960000   82.750000  \n",
              "max    1301.520000  194.780000  \n",
              "var   31789.565170  616.823046  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e291c84a-af8a-409b-8fd0-ece697869fe3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total</th>\n",
              "      <th>speeding</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>not_distracted</th>\n",
              "      <th>no_previous</th>\n",
              "      <th>ins_premium</th>\n",
              "      <th>ins_losses</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15.790196</td>\n",
              "      <td>4.998196</td>\n",
              "      <td>4.886784</td>\n",
              "      <td>13.573176</td>\n",
              "      <td>14.004882</td>\n",
              "      <td>886.957647</td>\n",
              "      <td>134.493137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.900000</td>\n",
              "      <td>1.792000</td>\n",
              "      <td>1.593000</td>\n",
              "      <td>1.760000</td>\n",
              "      <td>5.900000</td>\n",
              "      <td>641.960000</td>\n",
              "      <td>82.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23.900000</td>\n",
              "      <td>9.450000</td>\n",
              "      <td>10.038000</td>\n",
              "      <td>23.661000</td>\n",
              "      <td>21.280000</td>\n",
              "      <td>1301.520000</td>\n",
              "      <td>194.780000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>var</th>\n",
              "      <td>16.990902</td>\n",
              "      <td>4.071303</td>\n",
              "      <td>2.989901</td>\n",
              "      <td>20.330874</td>\n",
              "      <td>14.172755</td>\n",
              "      <td>31789.565170</td>\n",
              "      <td>616.823046</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e291c84a-af8a-409b-8fd0-ece697869fe3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e291c84a-af8a-409b-8fd0-ece697869fe3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e291c84a-af8a-409b-8fd0-ece697869fe3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    }
  ]
}